In [1]:
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
In [2]:
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
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nltk.download('all')
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[nltk_data]    |   Package vader_lexicon is already up-to-date!
[nltk_data]    | Downloading package porter_test to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all
Out[4]:
True
In [5]:
# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()
Out[5]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type
0 1 Snowpiercer 2013 18+ 6.9 94% NaN Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... Action,Drama,Sci-Fi,Thriller United States English Set seven years after the world has become a f... 60.0 tv series 3.0 1 0 0 0 1
1 2 Philadelphia 1993 13+ 8.8 80% NaN Charlie Day,Glenn Howerton,Rob McElhenney,Kait... Comedy United States English The gang, 5 raging alcoholic, narcissists run ... 22.0 tv series 18.0 1 0 0 0 1
2 3 Roma 2018 18+ 8.7 93% NaN Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... Action,Drama,History,Romance,War United Kingdom,United States English In this British historical drama, the turbulen... 52.0 tv series 2.0 1 0 0 0 1
3 4 Amy 2015 18+ 7.0 87% NaN Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... Drama United States English A family drama focused on three generations of... 60.0 tv series 6.0 1 0 1 1 1
4 5 The Young Offenders 2016 NaN 8.0 100% NaN Alex Murphy,Chris Walley,Hilary Rose,Dominic M... Comedy United Kingdom,Ireland English NaN 30.0 tv series 3.0 1 0 0 0 1
In [6]:
# profile = ProfileReport(df_tvshows)
# profile
In [7]:
def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()
In [8]:
data_investigate(df_tvshows)
No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation : 
In [9]:
# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)
In [10]:
data_investigate(df_tvshows)
No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation : 
In [11]:
df_tvshows.head()
Out[11]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 1 Snowpiercer 2013 18 6.9 94 NA Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... Action,Drama,Sci-Fi,Thriller United States ... Set seven years after the world has become a f... 60 tv series 3 1 0 0 0 1 Netflix
1 2 Philadelphia 1993 13 8.8 80 NA Charlie Day,Glenn Howerton,Rob McElhenney,Kait... Comedy United States ... The gang, 5 raging alcoholic, narcissists run ... 22 tv series 18 1 0 0 0 1 Netflix
2 3 Roma 2018 18 8.7 93 NA Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... Action,Drama,History,Romance,War United Kingdom,United States ... In this British historical drama, the turbulen... 52 tv series 2 1 0 0 0 1 Netflix
3 4 Amy 2015 18 7 87 NA Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... Drama United States ... A family drama focused on three generations of... 60 tv series 6 1 0 1 1 1 Netflix
4 5 The Young Offenders 2016 NR 8 100 NA Alex Murphy,Chris Walley,Hilary Rose,Dominic M... Comedy United Kingdom,Ireland ... NA 30 tv series 3 1 0 0 0 1 Netflix

5 rows × 21 columns

In [12]:
df_tvshows.describe()
Out[12]:
ID Year Netflix Hulu Prime Video Disney+ Type
count 5432.000000 5432.000000 5432.000000 5432.000000 5432.000000 5432.000000 5432.0
mean 2716.500000 2010.668446 0.341311 0.293999 0.403351 0.033689 1.0
std 1568.227662 11.726176 0.474193 0.455633 0.490615 0.180445 0.0
min 1.000000 1901.000000 0.000000 0.000000 0.000000 0.000000 1.0
25% 1358.750000 2009.000000 0.000000 0.000000 0.000000 0.000000 1.0
50% 2716.500000 2014.000000 0.000000 0.000000 0.000000 0.000000 1.0
75% 4074.250000 2017.000000 1.000000 1.000000 1.000000 0.000000 1.0
max 5432.000000 2020.000000 1.000000 1.000000 1.000000 1.000000 1.0
In [13]:
df_tvshows.corr()
Out[13]:
ID Year Netflix Hulu Prime Video Disney+ Type
ID 1.000000 -0.031346 -0.646330 0.034293 0.441264 0.195409 NaN
Year -0.031346 1.000000 0.222316 -0.065807 -0.198675 -0.022741 NaN
Netflix -0.646330 0.222316 1.000000 -0.366515 -0.515086 -0.119344 NaN
Hulu 0.034293 -0.065807 -0.366515 1.000000 -0.377374 -0.075701 NaN
Prime Video 0.441264 -0.198675 -0.515086 -0.377374 1.000000 -0.151442 NaN
Disney+ 0.195409 -0.022741 -0.119344 -0.075701 -0.151442 1.000000 NaN
Type NaN NaN NaN NaN NaN NaN NaN
In [14]:
# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)
In [15]:
# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows
In [16]:
# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]
In [17]:
df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]
In [18]:
df_tvshows_imdb = df_tvshows.copy()
In [19]:
df_tvshows_imdb.drop(df_tvshows_imdb.loc[df_tvshows_imdb['IMDb'] == "NA"].index, inplace = True)
# df_tvshows_imdb = df_tvshows_imdb[df_tvshows_imdb.IMDb != "NA"]
df_tvshows_imdb['IMDb'] = df_tvshows_imdb['IMDb'].astype(int)
In [20]:
# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_imdb_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['Netflix'] == 1]
hulu_imdb_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['Hulu'] == 1]
prime_video_imdb_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['Prime Video'] == 1]
disney_imdb_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['Disney+'] == 1]
In [21]:
df_tvshows_imdb_group = df_tvshows_imdb.copy()
In [22]:
plt.figure(figsize = (10, 10))
corr = df_tvshows_imdb.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
In [23]:
df_imdb_high_tvshows = df_tvshows_imdb.sort_values(by = 'IMDb', ascending = False).reset_index()
df_imdb_high_tvshows = df_imdb_high_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_imdb['IMDb'] == (df_tvshows_imdb['IMDb'].max()))
# df_imdb_high_tvshows = df_tvshows_imdb[filter]
 
# highest_rated_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['IMDb'].idxmax()]
 
print('\nTV Shows with Highest Ever IMDb  are : \n')
df_imdb_high_tvshows.head(5)
TV Shows with Highest Ever IMDb  are : 

Out[23]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 5129 Tanks! 2005 7 10 NA NA David Fletcher,Graham McTavish,John Erickson,B... History,War United States ... In this series Tom Hubbard travels throughout ... NA tv series 1 0 0 1 0 1 Prime Video
1 3718 The Sopranos 1999 18 9 92 NA James Gandolfini,Edie Falco,Michael Imperioli,... Crime,Drama United States ... Harry Bosch is an irreverent homicide detectiv... 55 tv series 6 0 0 1 0 1 Prime Video
2 144 The Chosen 1981 7 9 75 NA Shahar Isaac,Jonathan Roumie,Noah James,Paras ... Drama,History United States ... Depressed after the passing of his father (Dha... 54 tv series 2 0 0 1 0 1 Prime Video
3 835 Africa 2013 7 9 NA NA David Attenborough,Simon Blakeney,James Aldred... Documentary United Kingdom ... Three ordinary teenage girls discover a moon p... 360 tv series 1 1 0 0 0 1 Netflix
4 5304 The Imagineering Story 2019 0 9 100 NA Tom Morris,Kevin Rafferty,Angela Bassett,Tom F... Documentary United States ... Raven Baxter is a teenager. She can see glimps... 60 tv series 1 0 0 0 1 1 Disney+

5 rows × 21 columns

In [24]:
fig = px.bar(y = df_imdb_high_tvshows['Title'][:15],
             x = df_imdb_high_tvshows['IMDb'][:15], 
             color = df_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [25]:
df_imdb_low_tvshows = df_tvshows_imdb.sort_values(by = 'IMDb', ascending = True).reset_index()
df_imdb_low_tvshows = df_imdb_low_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_imdb['IMDb'] == (df_tvshows_imdb['IMDb'].min()))
# df_imdb_low_tvshows = df_tvshows_imdb[filter]

print('\nTV Shows with Lowest Ever IMDb  are : \n')
df_imdb_low_tvshows.head(5)
TV Shows with Lowest Ever IMDb  are : 

Out[25]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 5245 PINKFONG! 2017 NR 1 NA NA Marie Segoine Family NA ... NA NA tv series 1 0 0 1 0 1 Prime Video
1 3368 My Super Sweet 16 2005 7 1 NA NA Mary Morrison,Quincy Brown,Shad Moss,Cher Hubs... Documentary,Reality-TV United States ... Geologist Martin Pepper and Biologist Liz Bonn... 30 tv series 10 0 1 0 0 1 Hulu
2 3336 Toddlers & Tiaras 2009 7 1 NA NA MaKenzie Myers,Dawn Rochelle,Wendy D. Lee,Alan... Reality-TV United States ... NA NA tv series 7 0 1 1 0 1 Prime Video
3 4820 Pinkfong! Baby Shark Special 2017 NR 1 NA NA Marie Segoine Family NA ... The Bumble Nums are back with all new adventur... NA tv series 1 0 0 1 0 1 Prime Video
4 338 12-12-12 2012 18 1 NA NA Colin Murray,John Hartson,Robbie Savage Sport NA ... Hugo (Gabriel Byrne), heir to a fortune, is ma... NA tv series NA 0 0 1 0 1 Prime Video

5 rows × 21 columns

In [26]:
fig = px.bar(y = df_imdb_low_tvshows['Title'][:15],
             x = df_imdb_low_tvshows['IMDb'][:15], 
             color = df_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [27]:
print(f'''
      Total '{df_tvshows_imdb['IMDb'].unique().shape[0]}' unique IMDb s were Given, They were Like this,\n
      
{df_tvshows_imdb.sort_values(by = 'IMDb', ascending = False)['IMDb'].unique()}\n
 
      The Highest Ever IMDb Ever Any TV Show Got is '{df_imdb_high_tvshows['Title'][0]}' : '{df_imdb_high_tvshows['IMDb'].max()}'\n
 
      The Lowest Ever IMDb Ever Any TV Show Got is '{df_imdb_low_tvshows['Title'][0]}' : '{df_imdb_low_tvshows['IMDb'].min()}'\n
      ''')
      Total '10' unique IMDb s were Given, They were Like this,

      
[10  9  8  7  6  5  4  3  2  1]

 
      The Highest Ever IMDb Ever Any TV Show Got is 'Tanks!' : '10'

 
      The Lowest Ever IMDb Ever Any TV Show Got is 'PINKFONG!' : '1'

      
In [28]:
netflix_imdb_high_tvshows = df_imdb_high_tvshows.loc[df_imdb_high_tvshows['Netflix']==1].reset_index()
netflix_imdb_high_tvshows = netflix_imdb_high_tvshows.drop(['index'], axis = 1)
 
netflix_imdb_low_tvshows = df_imdb_low_tvshows.loc[df_imdb_low_tvshows['Netflix']==1].reset_index()
netflix_imdb_low_tvshows = netflix_imdb_low_tvshows.drop(['index'], axis = 1)
 
netflix_imdb_high_tvshows.head(5)
Out[28]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 835 Africa 2013 7 9 NA NA David Attenborough,Simon Blakeney,James Aldred... Documentary United Kingdom ... Three ordinary teenage girls discover a moon p... 360 tv series 1 1 0 0 0 1 Netflix
1 779 Yeh Meri Family 2018 16 9 NA NA Vishesh Bansal,Mona Singh,Akarsh Khurana,Ahan ... Comedy,Drama,Family,Musical,Romance India ... Three teen strangers wake up in a mysterious l... 30 tv series 1 1 0 0 0 1 Netflix
2 1576 Raja Rasoi Aur Anya Kahaniyan 2015 NR 9 NA NA Manwendra Tripathy History India ... Amamiya Shuuhei moves from Tokyo to the countr... 45 tv series 2 1 0 1 0 1 Netflix
3 1212 Ch:os:en 2013 7 9 NA NA Shahar Isaac,Jonathan Roumie,Noah James,Paras ... Drama,History United States ... A madly intense whirlwind drama about love, be... 54 tv series 2 1 0 0 0 1 Netflix
4 505 Breaking Bad 2008 16 9 96 NA Bryan Cranston,Anna Gunn,Aaron Paul,Betsy Bran... Crime,Drama,Thriller United States ... When chemistry teacher Walter White is diagnos... 49 tv series 5 1 0 0 0 1 Netflix

5 rows × 21 columns

In [29]:
fig = px.bar(y = netflix_imdb_high_tvshows['Title'][:15],
             x = netflix_imdb_high_tvshows['IMDb'][:15], 
             color = netflix_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [30]:
fig = px.bar(y = netflix_imdb_low_tvshows['Title'][:15],
             x = netflix_imdb_low_tvshows['IMDb'][:15], 
             color = netflix_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [31]:
hulu_imdb_high_tvshows = df_imdb_high_tvshows.loc[df_imdb_high_tvshows['Hulu']==1].reset_index()
hulu_imdb_high_tvshows = hulu_imdb_high_tvshows.drop(['index'], axis = 1)
 
hulu_imdb_low_tvshows = df_imdb_low_tvshows.loc[df_imdb_low_tvshows['Hulu']==1].reset_index()
hulu_imdb_low_tvshows = hulu_imdb_low_tvshows.drop(['index'], axis = 1)
 
hulu_imdb_high_tvshows.head(5)
Out[31]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 3395 Hungry Henry 2014 NR 9 NA NA NA NA United States ... NA 11 tv series 2 0 1 0 0 1 Hulu
1 2694 Land of Honor 2014 16 9 NA NA Jun Gong,Zhehan Zhang,Ye Zhou,Gong Jun,Ma Wen ... Action,Drama,Fantasy,History China ... The Haves and the Have Nots is a primetime cab... NA tv series 1 0 1 0 0 1 Hulu
2 589 Death Note 2006 18 9 100 NA Mamoru Miyano,Brad Swaile,Vincent Tong,Ryô Nai... Animation,Crime,Drama,Fantasy,Mystery,Thriller Japan ... On Nadia's 36th birthday she is struck by a ca... 24 tv series 1 1 1 0 0 1 Netflix
3 2664 The Joy of Painting 1983 0 9 NA NA Bob Ross,Steve Ross,Dana Jester,Peep,John Tham... Documentary,Family United States ... The Winslow family is a pretty normal family e... 30 tv series 31 0 1 1 0 1 Prime Video
4 3606 The Adventures of Dr. Buckeye Bottoms 2017 NR 9 NA NA Bartholomew Buckeye Bottoms,Zac Fine Reality-TV United States ... NA NA tv series 2 0 1 0 0 1 Hulu

5 rows × 21 columns

In [32]:
fig = px.bar(y = hulu_imdb_high_tvshows['Title'][:15],
             x = hulu_imdb_high_tvshows['IMDb'][:15], 
             color = hulu_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [33]:
fig = px.bar(y = hulu_imdb_low_tvshows['Title'][:15],
             x = hulu_imdb_low_tvshows['IMDb'][:15], 
             color = hulu_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [34]:
prime_video_imdb_high_tvshows = df_imdb_high_tvshows.loc[df_imdb_high_tvshows['Prime Video']==1].reset_index()
prime_video_imdb_high_tvshows = prime_video_imdb_high_tvshows.drop(['index'], axis = 1)
 
prime_video_imdb_low_tvshows = df_imdb_low_tvshows.loc[df_imdb_low_tvshows['Prime Video']==1].reset_index()
prime_video_imdb_low_tvshows = prime_video_imdb_low_tvshows.drop(['index'], axis = 1)
 
prime_video_imdb_high_tvshows.head(5)
Out[34]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 5129 Tanks! 2005 7 10 NA NA David Fletcher,Graham McTavish,John Erickson,B... History,War United States ... In this series Tom Hubbard travels throughout ... NA tv series 1 0 0 1 0 1 Prime Video
1 3718 The Sopranos 1999 18 9 92 NA James Gandolfini,Edie Falco,Michael Imperioli,... Crime,Drama United States ... Harry Bosch is an irreverent homicide detectiv... 55 tv series 6 0 0 1 0 1 Prime Video
2 144 The Chosen 1981 7 9 75 NA Shahar Isaac,Jonathan Roumie,Noah James,Paras ... Drama,History United States ... Depressed after the passing of his father (Dha... 54 tv series 2 0 0 1 0 1 Prime Video
3 4124 Harmony with A R Rahman 2018 NR 9 NA NA A.R. Rahman,Ustad Mohi Baha'uddin Dagar,Sajith... Documentary,Musical India ... Our thirties and forties are the busiest time ... NA tv series 1 0 0 1 0 1 Prime Video
4 1576 Raja Rasoi Aur Anya Kahaniyan 2015 NR 9 NA NA Manwendra Tripathy History India ... Amamiya Shuuhei moves from Tokyo to the countr... 45 tv series 2 1 0 1 0 1 Netflix

5 rows × 21 columns

In [35]:
fig = px.bar(y = prime_video_imdb_high_tvshows['Title'][:15],
             x = prime_video_imdb_high_tvshows['IMDb'][:15], 
             color = prime_video_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [36]:
fig = px.bar(y = prime_video_imdb_low_tvshows['Title'][:15],
             x = prime_video_imdb_low_tvshows['IMDb'][:15], 
             color = prime_video_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [37]:
disney_imdb_high_tvshows = df_imdb_high_tvshows.loc[df_imdb_high_tvshows['Disney+']==1].reset_index()
disney_imdb_high_tvshows = disney_imdb_high_tvshows.drop(['index'], axis = 1)
 
disney_imdb_low_tvshows = df_imdb_low_tvshows.loc[df_imdb_low_tvshows['Disney+']==1].reset_index()
disney_imdb_low_tvshows = disney_imdb_low_tvshows.drop(['index'], axis = 1)
 
disney_imdb_high_tvshows.head(5)
Out[37]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 5304 The Imagineering Story 2019 0 9 100 NA Tom Morris,Kevin Rafferty,Angela Bassett,Tom F... Documentary United States ... Raven Baxter is a teenager. She can see glimps... 60 tv series 1 0 0 0 1 1 Disney+
1 493 The Other Me 2000 0 9 NA Sotiris Tsafoulias Pigmalion Dadakaridis,Petros Lagoutis,Vicky Pa... Crime,Drama,Mystery,Thriller Greece ... NA 45 tv series 2 0 0 0 1 1 Disney+
2 483 Avatar 2009 13 9 82 NA Dee Bradley Baker,Zach Tyler,Mae Whitman,Jack ... Animation,Action,Adventure,Family,Fantasy,Mystery United States ... In a suburban fantasy world, two teenage elf b... 23 tv series 3 0 0 0 1 1 Disney+
3 1037 Brain Games 2011 0 8 NA NA Jason Silva,Bert Thomas Morris,Apollo Robbins,... Documentary,Comedy,Drama,Game-Show,Reality-TV United States ... NA 60 tv series 6 1 0 0 1 1 Netflix
4 2490 Star vs. the Forces of Evil 2015 7 8 NA NA Eden Sher,Adam McArthur,Grey Griffin,Daron Nef... Animation,Action,Adventure,Comedy,Drama,Family... United States,Spain,United Kingdom,Mexico,Japan ... Set in 2008 and against the hugely atmospheric... 22 tv series 4 0 1 0 1 1 Disney+

5 rows × 21 columns

In [38]:
fig = px.bar(y = disney_imdb_high_tvshows['Title'][:15],
             x = disney_imdb_high_tvshows['IMDb'][:15], 
             color = disney_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [39]:
fig = px.bar(y = disney_imdb_low_tvshows['Title'][:15],
             x = disney_imdb_low_tvshows['IMDb'][:15], 
             color = disney_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [40]:
print(f'''
      The TV Show with Highest IMDb  Ever Got is '{df_imdb_high_tvshows['Title'][0]}' : '{df_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  Ever Got is '{df_imdb_low_tvshows['Title'][0]}' : '{df_imdb_low_tvshows['IMDb'].min()}'\n
      
      The TV Show with Highest IMDb  on 'Netflix' is '{netflix_imdb_high_tvshows['Title'][0]}' : '{netflix_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  on 'Netflix' is '{netflix_imdb_low_tvshows['Title'][0]}' : '{netflix_imdb_low_tvshows['IMDb'].min()}'\n
      
      The TV Show with Highest IMDb  on 'Hulu' is '{hulu_imdb_high_tvshows['Title'][0]}' : '{hulu_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  on 'Hulu' is '{hulu_imdb_low_tvshows['Title'][0]}' : '{hulu_imdb_low_tvshows['IMDb'].min()}'\n
      
      The TV Show with Highest IMDb  on 'Prime Video' is '{prime_video_imdb_high_tvshows['Title'][0]}' : '{prime_video_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  on 'Prime Video' is '{prime_video_imdb_low_tvshows['Title'][0]}' : '{prime_video_imdb_low_tvshows['IMDb'].min()}'\n
      
      The TV Show with Highest IMDb  on 'Disney+' is '{disney_imdb_high_tvshows['Title'][0]}' : '{disney_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  on 'Disney+' is '{disney_imdb_low_tvshows['Title'][0]}' : '{disney_imdb_low_tvshows['IMDb'].min()}'\n 
      ''')
      The TV Show with Highest IMDb  Ever Got is 'Tanks!' : '10'

      The TV Show with Lowest IMDb  Ever Got is 'PINKFONG!' : '1'

      
      The TV Show with Highest IMDb  on 'Netflix' is 'Africa' : '9'

      The TV Show with Lowest IMDb  on 'Netflix' is 'Game Winning Hit' : '2'

      
      The TV Show with Highest IMDb  on 'Hulu' is 'Hungry Henry' : '9'

      The TV Show with Lowest IMDb  on 'Hulu' is 'My Super Sweet 16' : '1'

      
      The TV Show with Highest IMDb  on 'Prime Video' is 'Tanks!' : '10'

      The TV Show with Lowest IMDb  on 'Prime Video' is 'PINKFONG!' : '1'

      
      The TV Show with Highest IMDb  on 'Disney+' is 'The Imagineering Story' : '9'

      The TV Show with Lowest IMDb  on 'Disney+' is 'Bizaardvark' : '3'
 
      
In [41]:
print(f'''
      Accross All Platforms the Average IMDb  is '{round(df_tvshows_imdb['IMDb'].mean(), ndigits = 2)}'\n
      The Average IMDb  on 'Netflix' is '{round(netflix_imdb_tvshows['IMDb'].mean(), ndigits = 2)}'\n
      The Average IMDb  on 'Hulu' is '{round(hulu_imdb_tvshows['IMDb'].mean(), ndigits = 2)}'\n
      The Average IMDb  on 'Prime Video' is '{round(prime_video_imdb_tvshows['IMDb'].mean(), ndigits = 2)}'\n
      The Average IMDb  on 'Disney+' is '{round(disney_imdb_tvshows['IMDb'].mean(), ndigits = 2)}'\n 
      ''')
      Accross All Platforms the Average IMDb  is '6.7'

      The Average IMDb  on 'Netflix' is '6.77'

      The Average IMDb  on 'Hulu' is '6.64'

      The Average IMDb  on 'Prime Video' is '6.71'

      The Average IMDb  on 'Disney+' is '6.58'
 
      
In [42]:
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_imdb['IMDb'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_imdb['IMDb'], ax = ax[1])
plt.show()
In [43]:
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('IMDb s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_imdb_tvshows['IMDb'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_imdb_tvshows['IMDb'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_imdb_tvshows['IMDb'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_imdb_tvshows['IMDb'][:100], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
In [44]:
def round_val(data):
    if str(data) != 'nan':
        return round(data)
In [45]:
df_tvshows_imdb_group['IMDb Group'] = df_tvshows_imdb['IMDb'].apply(round_val)
 
imdb_values = df_tvshows_imdb_group['IMDb Group'].value_counts().sort_index(ascending = False).tolist()
imdb_index = df_tvshows_imdb_group['IMDb Group'].value_counts().sort_index(ascending = False).index
 
# imdb_values, imdb_index
In [46]:
imdb_group_count = df_tvshows_imdb_group.groupby('IMDb Group')['Title'].count()
imdb_group_tvshows = df_tvshows_imdb_group.groupby('IMDb Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
imdb_group_data_tvshows = pd.concat([imdb_group_count, imdb_group_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
imdb_group_data_tvshows = imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)
In [47]:
# IMDb Group with TV Shows Counts - All Platforms Combined
imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)
Out[47]:
IMDb Group TV Shows Count Netflix Hulu Prime Video Disney+
6 7 1956 737 603 694 53
7 8 1147 456 339 432 41
5 6 1065 415 316 349 55
4 5 404 139 119 156 22
3 4 163 45 57 62 7
2 3 61 17 25 23 1
8 9 53 18 12 27 3
1 2 19 4 10 6 0
0 1 7 0 3 5 0
9 10 1 0 0 1 0
In [48]:
imdb_group_data_tvshows.sort_values(by = 'IMDb Group', ascending = False)
Out[48]:
IMDb Group TV Shows Count Netflix Hulu Prime Video Disney+
9 10 1 0 0 1 0
8 9 53 18 12 27 3
7 8 1147 456 339 432 41
6 7 1956 737 603 694 53
5 6 1065 415 316 349 55
4 5 404 139 119 156 22
3 4 163 45 57 62 7
2 3 61 17 25 23 1
1 2 19 4 10 6 0
0 1 7 0 3 5 0
In [49]:
fig = px.bar(y = imdb_group_data_tvshows['TV Shows Count'],
             x = imdb_group_data_tvshows['IMDb Group'], 
             color = imdb_group_data_tvshows['IMDb Group'],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Group IMDb : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()
In [50]:
fig = px.pie(imdb_group_data_tvshows[:10],
             names = imdb_group_data_tvshows['IMDb Group'],
             values = imdb_group_data_tvshows['TV Shows Count'],
             color = imdb_group_data_tvshows['TV Shows Count'],
             color_discrete_sequence = px.colors.sequential.Teal)

fig.update_traces(textinfo = 'percent+label',
                  title = 'TV Shows Count based on IMDb Group')
fig.show()
In [51]:
df_imdb_group_high_tvshows = imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_imdb_group_high_tvshows = df_imdb_group_high_tvshows.drop(['index'], axis = 1)
# filter = (imdb_group_data_tvshows['TV Shows Count'] ==  (imdb_group_data_tvshows['TV Shows Count'].max()))
# df_imdb_group_high_tvshows = imdb_group_data_tvshows[filter]
 
# highest_rated_tvshows = imdb_group_data_tvshows.loc[imdb_group_data_tvshows['TV Shows Count'].idxmax()]
 
# print('\nIMDb with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_imdb_group_high_tvshows.head(5)
Out[51]:
IMDb Group TV Shows Count Netflix Hulu Prime Video Disney+
0 7 1956 737 603 694 53
1 8 1147 456 339 432 41
2 6 1065 415 316 349 55
3 5 404 139 119 156 22
4 4 163 45 57 62 7
In [52]:
df_imdb_group_low_tvshows = imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_imdb_group_low_tvshows = df_imdb_group_low_tvshows.drop(['index'], axis = 1)
# filter = (imdb_group_data_tvshows['TV Shows Count'] = =  (imdb_group_data_tvshows['TV Shows Count'].min()))
# df_imdb_group_low_tvshows = imdb_group_data_tvshows[filter]
 
# print('\nIMDb with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_imdb_group_low_tvshows.head(5)
Out[52]:
IMDb Group TV Shows Count Netflix Hulu Prime Video Disney+
0 10 1 0 0 1 0
1 1 7 0 3 5 0
2 2 19 4 10 6 0
3 9 53 18 12 27 3
4 3 61 17 25 23 1
In [53]:
print(f'''
      Total '{df_tvshows_imdb['IMDb'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{imdb_group_data_tvshows['IMDb Group'].unique().shape[0]}' IMDb Group, They were Like this, \n
 
      {imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['IMDb Group'].unique()} etc. \n
 
      The IMDb Group with Highest TV Shows Count have '{imdb_group_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_imdb_group_high_tvshows['IMDb Group'][0]}', &\n
      The IMDb Group with Lowest TV Shows Count have '{imdb_group_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_imdb_group_low_tvshows['IMDb Group'][0]}'
      ''')
      Total '4876' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '10' IMDb Group, They were Like this, 

 
      [ 7  8  6  5  4  3  9  2  1 10] etc. 

 
      The IMDb Group with Highest TV Shows Count have '1956' TV Shows Available is '7', &

      The IMDb Group with Lowest TV Shows Count have '1' TV Shows Available is '10'
      
In [54]:
netflix_imdb_group_tvshows = imdb_group_data_tvshows[imdb_group_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_imdb_group_tvshows = netflix_imdb_group_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_imdb_group_high_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_imdb_group_high_tvshows = netflix_imdb_group_high_tvshows.drop(['index'], axis = 1)
 
netflix_imdb_group_low_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_imdb_group_low_tvshows = netflix_imdb_group_low_tvshows.drop(['index'], axis = 1)
 
netflix_imdb_group_high_tvshows.head(5)
Out[54]:
IMDb Group TV Shows Count Netflix Hulu Prime Video Disney+
0 7 1956 737 603 694 53
1 8 1147 456 339 432 41
2 6 1065 415 316 349 55
3 5 404 139 119 156 22
4 4 163 45 57 62 7
In [55]:
hulu_imdb_group_tvshows = imdb_group_data_tvshows[imdb_group_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_imdb_group_tvshows = hulu_imdb_group_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_imdb_group_high_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_imdb_group_high_tvshows = hulu_imdb_group_high_tvshows.drop(['index'], axis = 1)
 
hulu_imdb_group_low_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_imdb_group_low_tvshows = hulu_imdb_group_low_tvshows.drop(['index'], axis = 1)
 
hulu_imdb_group_high_tvshows.head(5)
Out[55]:
IMDb Group TV Shows Count Netflix Hulu Prime Video Disney+
0 7 1956 737 603 694 53
1 8 1147 456 339 432 41
2 6 1065 415 316 349 55
3 5 404 139 119 156 22
4 4 163 45 57 62 7
In [56]:
prime_video_imdb_group_tvshows = imdb_group_data_tvshows[imdb_group_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_imdb_group_tvshows = prime_video_imdb_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_imdb_group_high_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_imdb_group_high_tvshows = prime_video_imdb_group_high_tvshows.drop(['index'], axis = 1)
 
prime_video_imdb_group_low_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_imdb_group_low_tvshows = prime_video_imdb_group_low_tvshows.drop(['index'], axis = 1)
 
prime_video_imdb_group_high_tvshows.head(5)
Out[56]:
IMDb Group TV Shows Count Netflix Hulu Prime Video Disney+
0 7 1956 737 603 694 53
1 8 1147 456 339 432 41
2 6 1065 415 316 349 55
3 5 404 139 119 156 22
4 4 163 45 57 62 7
In [57]:
disney_imdb_group_tvshows = imdb_group_data_tvshows[imdb_group_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_imdb_group_tvshows = disney_imdb_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_imdb_group_high_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_imdb_group_high_tvshows = disney_imdb_group_high_tvshows.drop(['index'], axis = 1)
 
disney_imdb_group_low_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_imdb_group_low_tvshows = disney_imdb_group_low_tvshows.drop(['index'], axis = 1)
 
disney_imdb_group_high_tvshows.head(5)
Out[57]:
IMDb Group TV Shows Count Netflix Hulu Prime Video Disney+
0 6 1065 415 316 349 55
1 7 1956 737 603 694 53
2 8 1147 456 339 432 41
3 5 404 139 119 156 22
4 4 163 45 57 62 7
In [58]:
print(f'''
      The IMDb Group with Highest TV Shows Count Ever Got is '{df_imdb_group_high_tvshows['IMDb Group'][0]}' : '{df_imdb_group_high_tvshows['TV Shows Count'].max()}'\n
      The IMDb Group with Lowest TV Shows Count Ever Got is '{df_imdb_group_low_tvshows['IMDb Group'][0]}' : '{df_imdb_group_low_tvshows['TV Shows Count'].min()}'\n
      
      The IMDb Group with Highest TV Shows Count on 'Netflix' is '{netflix_imdb_group_high_tvshows['IMDb Group'][0]}' : '{netflix_imdb_group_high_tvshows['Netflix'].max()}'\n
      The IMDb Group with Lowest TV Shows Count on 'Netflix' is '{netflix_imdb_group_low_tvshows['IMDb Group'][0]}' : '{netflix_imdb_group_low_tvshows['Netflix'].min()}'\n
      
      The IMDb Group with Highest TV Shows Count on 'Hulu' is '{hulu_imdb_group_high_tvshows['IMDb Group'][0]}' : '{hulu_imdb_group_high_tvshows['Hulu'].max()}'\n
      The IMDb Group with Lowest TV Shows Count on 'Hulu' is '{hulu_imdb_group_low_tvshows['IMDb Group'][0]}' : '{hulu_imdb_group_low_tvshows['Hulu'].min()}'\n
      
      The IMDb Group with Highest TV Shows Count on 'Prime Video' is '{prime_video_imdb_group_high_tvshows['IMDb Group'][0]}' : '{prime_video_imdb_group_high_tvshows['Prime Video'].max()}'\n
      The IMDb Group with Lowest TV Shows Count on 'Prime Video' is '{prime_video_imdb_group_low_tvshows['IMDb Group'][0]}' : '{prime_video_imdb_group_low_tvshows['Prime Video'].min()}'\n
      
      The IMDb Group with Highest TV Shows Count on 'Disney+' is '{disney_imdb_group_high_tvshows['IMDb Group'][0]}' : '{disney_imdb_group_high_tvshows['Disney+'].max()}'\n
      The IMDb Group with Lowest TV Shows Count on 'Disney+' is '{disney_imdb_group_low_tvshows['IMDb Group'][0]}' : '{disney_imdb_group_low_tvshows['Disney+'].min()}'\n 
      ''')
      The IMDb Group with Highest TV Shows Count Ever Got is '7' : '1956'

      The IMDb Group with Lowest TV Shows Count Ever Got is '10' : '1'

      
      The IMDb Group with Highest TV Shows Count on 'Netflix' is '7' : '737'

      The IMDb Group with Lowest TV Shows Count on 'Netflix' is '1' : '0'

      
      The IMDb Group with Highest TV Shows Count on 'Hulu' is '7' : '603'

      The IMDb Group with Lowest TV Shows Count on 'Hulu' is '10' : '0'

      
      The IMDb Group with Highest TV Shows Count on 'Prime Video' is '7' : '694'

      The IMDb Group with Lowest TV Shows Count on 'Prime Video' is '10' : '1'

      
      The IMDb Group with Highest TV Shows Count on 'Disney+' is '6' : '55'

      The IMDb Group with Lowest TV Shows Count on 'Disney+' is '2' : '0'
 
      
In [59]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_i_ax1 = sns.barplot(x = netflix_imdb_group_tvshows['IMDb Group'][:10], y = netflix_imdb_group_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_i_ax2 = sns.barplot(x = hulu_imdb_group_tvshows['IMDb Group'][:10], y = hulu_imdb_group_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_i_ax3 = sns.barplot(x = prime_video_imdb_group_tvshows['IMDb Group'][:10], y = prime_video_imdb_group_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_i_ax4 = sns.barplot(x = disney_imdb_group_tvshows['IMDb Group'][:10], y = disney_imdb_group_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])
 
plt.show()
In [60]:
plt.figure(figsize = (20, 5))
sns.lineplot(x = imdb_group_data_tvshows['IMDb Group'], y = imdb_group_data_tvshows['Netflix'], color = 'red')
sns.lineplot(x = imdb_group_data_tvshows['IMDb Group'], y = imdb_group_data_tvshows['Hulu'], color = 'lightgreen')
sns.lineplot(x = imdb_group_data_tvshows['IMDb Group'], y = imdb_group_data_tvshows['Prime Video'], color = 'lightblue')
sns.lineplot(x = imdb_group_data_tvshows['IMDb Group'], y = imdb_group_data_tvshows['Disney+'], color = 'darkblue')
plt.xlabel('IMDb Group', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()
In [61]:
print(f'''
      Accross All Platforms Total Count of IMDb Group is '{imdb_group_data_tvshows['IMDb Group'].unique().shape[0]}'\n
      Total Count of IMDb Group on 'Netflix' is '{netflix_imdb_group_tvshows['IMDb Group'].unique().shape[0]}'\n
      Total Count of IMDb Group on 'Hulu' is '{hulu_imdb_group_tvshows['IMDb Group'].unique().shape[0]}'\n
      Total Count of IMDb Group on 'Prime Video' is '{prime_video_imdb_group_tvshows['IMDb Group'].unique().shape[0]}'\n
      Total Count of IMDb Group on 'Disney+' is '{disney_imdb_group_tvshows['IMDb Group'].unique().shape[0]}'\n 
      ''')
      Accross All Platforms Total Count of IMDb Group is '10'

      Total Count of IMDb Group on 'Netflix' is '8'

      Total Count of IMDb Group on 'Hulu' is '9'

      Total Count of IMDb Group on 'Prime Video' is '10'

      Total Count of IMDb Group on 'Disney+' is '7'
 
      
In [62]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_i_ax1 = sns.lineplot(y = imdb_group_data_tvshows['IMDb Group'], x = imdb_group_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_i_ax2 = sns.lineplot(y = imdb_group_data_tvshows['IMDb Group'], x = imdb_group_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_i_ax3 = sns.lineplot(y = imdb_group_data_tvshows['IMDb Group'], x = imdb_group_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_i_ax4 = sns.lineplot(y = imdb_group_data_tvshows['IMDb Group'], x = imdb_group_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])

plt.show()
In [63]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_i_ax1 = sns.barplot(x = imdb_group_data_tvshows['IMDb Group'][:10], y = imdb_group_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_i_ax2 = sns.barplot(x = imdb_group_data_tvshows['IMDb Group'][:10], y = imdb_group_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_i_ax3 = sns.barplot(x = imdb_group_data_tvshows['IMDb Group'][:10], y = imdb_group_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_i_ax4 = sns.barplot(x = imdb_group_data_tvshows['IMDb Group'][:10], y = imdb_group_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])
 
plt.show()